APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS
This article explores traffic management strategies for addressing unpredictable events in transportation networks, focusing on situations where road segment capacity is reduced due to factors like traffic accidents or disruptions. The research aims to determine the proportion of traffic flow redist...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Silesian University of Technology
2025-06-01
|
| Series: | Scientific Journal of Silesian University of Technology. Series Transport |
| Subjects: | |
| Online Access: | https://sjsutst.polsl.pl/archives/2025/vol127/267_SJSUTST127_2025_Nguyen_Than_Pham_Nguyen_Ha.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839630260103020544 |
|---|---|
| author | Xuan-Hien NGUYEN Thi Van Anh VU Quoc Viet THAN Viet Thanh PHAM The Anh NGUYEN Muon HA |
| author_facet | Xuan-Hien NGUYEN Thi Van Anh VU Quoc Viet THAN Viet Thanh PHAM The Anh NGUYEN Muon HA |
| author_sort | Xuan-Hien NGUYEN |
| collection | DOAJ |
| description | This article explores traffic management strategies for addressing unpredictable events in transportation networks, focusing on situations where road segment capacity is reduced due to factors like traffic accidents or disruptions. The research aims to determine the proportion of traffic flow redistribution needed to maintain network efficiency under such conditions. A novel method is proposed to mitigate congestion by rerouting vehicles from heavily loaded roads, identified by high network load coefficients, to alternative routes. The approach also calculates the optimal volume of redirected traffic to avoid overloading other parts of the network, thereby minimizing the risk of secondary congestion. To achieve this, neural network-based survey and regression analysis techniques are utilized, offering precise and data-driven solutions for traffic redirection. The study highlights the potential of improving urban traffic flow through enhancements to indirect traffic control systems integrated into Intelligent Transportation Systems. By optimizing vehicle rerouting strategies, the proposed method seeks to increase ITS efficiency, especially in scenarios with high congestion risks or traffic accidents. This approach promises a more resilient and adaptive urban transportation network, ensuring smoother traffic operations and reduced congestion impacts.
|
| format | Article |
| id | doaj-art-fba63bcc26014039b2b85fbdf7c878d5 |
| institution | Matheson Library |
| issn | 0209-3324 2450-1549 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Silesian University of Technology |
| record_format | Article |
| series | Scientific Journal of Silesian University of Technology. Series Transport |
| spelling | doaj-art-fba63bcc26014039b2b85fbdf7c878d52025-07-14T06:46:56ZengSilesian University of TechnologyScientific Journal of Silesian University of Technology. Series Transport0209-33242450-15492025-06-0112726727510.20858/sjsutst.2025.127.16APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKSXuan-Hien NGUYENThi Van Anh VUQuoc Viet THANViet Thanh PHAMThe Anh NGUYENMuon HAThis article explores traffic management strategies for addressing unpredictable events in transportation networks, focusing on situations where road segment capacity is reduced due to factors like traffic accidents or disruptions. The research aims to determine the proportion of traffic flow redistribution needed to maintain network efficiency under such conditions. A novel method is proposed to mitigate congestion by rerouting vehicles from heavily loaded roads, identified by high network load coefficients, to alternative routes. The approach also calculates the optimal volume of redirected traffic to avoid overloading other parts of the network, thereby minimizing the risk of secondary congestion. To achieve this, neural network-based survey and regression analysis techniques are utilized, offering precise and data-driven solutions for traffic redirection. The study highlights the potential of improving urban traffic flow through enhancements to indirect traffic control systems integrated into Intelligent Transportation Systems. By optimizing vehicle rerouting strategies, the proposed method seeks to increase ITS efficiency, especially in scenarios with high congestion risks or traffic accidents. This approach promises a more resilient and adaptive urban transportation network, ensuring smoother traffic operations and reduced congestion impacts. https://sjsutst.polsl.pl/archives/2025/vol127/267_SJSUTST127_2025_Nguyen_Than_Pham_Nguyen_Ha.pdftraffic managementintelligent transportation systemsintelligent neutral networktraffic flows |
| spellingShingle | Xuan-Hien NGUYEN Thi Van Anh VU Quoc Viet THAN Viet Thanh PHAM The Anh NGUYEN Muon HA APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS Scientific Journal of Silesian University of Technology. Series Transport traffic management intelligent transportation systems intelligent neutral network traffic flows |
| title | APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS |
| title_full | APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS |
| title_fullStr | APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS |
| title_full_unstemmed | APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS |
| title_short | APPLYING NEURAL NETWORK TECHNIQUES TO DETERMINE TRAFFIC FLOW REDIRECTION PROPORTIONS IN ROAD NETWORKS |
| title_sort | applying neural network techniques to determine traffic flow redirection proportions in road networks |
| topic | traffic management intelligent transportation systems intelligent neutral network traffic flows |
| url | https://sjsutst.polsl.pl/archives/2025/vol127/267_SJSUTST127_2025_Nguyen_Than_Pham_Nguyen_Ha.pdf |
| work_keys_str_mv | AT xuanhiennguyen applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks AT thivananhvu applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks AT quocvietthan applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks AT vietthanhpham applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks AT theanhnguyen applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks AT muonha applyingneuralnetworktechniquestodeterminetrafficflowredirectionproportionsinroadnetworks |